ShieldSquare is now Radware Bot Manager

ShieldSquare is now Radware Bot Manager

Why In-house Bot Detection Fails Online Businesses

January 18, 2019 | All Automated Threats Bot Prevention Technologies


Bad bots and botnets are the latest addition to the menacing world of cybersecurity threats. Though the problem was grave since the early 2010s, it was during 2016’s US Presidential Election when the world actually realized the impact that bots can create. In recent years, the constant struggle of online businesses (including social media giants such as Facebook and Twitter) against bad bots has deepened without any notable success. Conventional security measures and in-house detection systems have failed online businesses in providing requisite defense against bad bots.

From our interactions with CISOs and marketers from across the world, we learned that in-house measures are the first solution that any online business applies as soon as it faces the wrath of bad bots. Much to their dismay, in-house solutions often fail to recognize sophisticated bot patterns — and cause false positives/negatives as they lack collective intelligence, historical data — and advanced machine learning based bot detection techniques that are perfected over the years to ensure minimal false positives and negatives. Here in this blog, let’s examine where in-house solutions fall short of the capabilities needed to accurately manage bots.

4 Reasons to Choose a Bot Management Specialist Over In-house Bot Detection

Must-have Capabilities to Detect and Block Bad Bots

In-house Bot Detection

Bot Management Specialists

Ability to Detect Large-scale Distributed Attacks as well as Low and Slow Attacks


Can only detect a small fraction of level 1, 2 and 3 bots that are coming from already defined IP addresses.*


Dedicated solutions can detect all types of large-scale as well as low and slow bad bots including constantly evolving level 4 bots that often utilize RPA and machine learning.

Capability to Understand Intent behind Attacks


Only large enterprises can afford to invest in behavior analysis for detection. In-house solutions also struggle to go beyond mouse movements and keystrokes to understand the intent behind attacks.


For example, Radware Bot Manager’s Intent-based deep Behavior Analysis (IDBA) reads beyond mouse movements and keystrokes to understand the intent of visitors, detect bad bots, and avert potential attacks. Reach us to know more.

Custom Responses and URL/Section-based Bot Management


In-house solutions are based on a predetermined set of rules and are not flexible for customized actions.


With dedicated solutions such as Radware Bot Manager, you can block the bots visiting login URLs, feed fake or misleading data on the product or listing pages, take any custom action or challenge bots with a CAPTCHA to ensure zero false positives.

Minimal False Positives/Negatives

High False Positives/Negatives

In-house detection systems lack the ability to consider distinctive behavior of highly-active genuine users as they don’t have historical data to analyze user behavior and in such scenarios, in-house detection systems end up blocking a significant number of genuine users.

Minimal False Positives/Negatives.

Dedicated bot management solutions analyze each and every visitors based on their behavior and origin to ensure that false positives/negatives are not caused.

*Read Page #10 of Radware Bot Manager’s The Big, Bad Bot Problem report to learn more about level 1, 2, 3, and 4 bots.

Apart from these 4 must-have capabilities, an ideal bot management solution should also offer many other features such as integration with popular analytics platforms to filter bad bot traffic from analytics, UTM-based management, layered protection, and to name a few more. You can read our e-book on how to evaluate bot management solution to learn more.

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